Interpretation des predictions appartements
Author : VotreNom
Description : Rapport Shapash pour appartements
Project_Name : Analyse lightgbm_appart
Model used : LGBMRegressor
Library : lightgbm.sklearn
Library version : 4.6.0
Model parameters :
| Parameter key | Parameter value |
|---|---|
| boosting_type | gbdt |
| objective | None |
| num_leaves | 95 |
| max_depth | 10 |
| learning_rate | 0.06774931487278521 |
| n_estimators | 294 |
| subsample_for_bin | 200000 |
| min_split_gain | 0.0 |
| min_child_weight | 0.001 |
| min_child_samples | 10 |
| subsample | 1.0 |
| subsample_freq | 0 |
| colsample_bytree | 1.0 |
| reg_alpha | 0.0 |
| reg_lambda | 0.0 |
| random_state | None |
| Parameter key | Parameter value |
|---|---|
| n_jobs | None |
| importance_type | split |
| _Booster | |
| _evals_result | {} |
| _best_score | defaultdict( |
| _best_iteration | 0 |
| _other_params | {} |
| _objective | regression |
| class_weight | None |
| _class_weight | None |
| _class_map | None |
| _n_features | 56 |
| _n_features_in | 56 |
| _classes | None |
| _n_classes | -1 |
| fitted_ | True |
| Training dataset | Prediction dataset | |
|---|---|---|
| number of features | NaN | 56 |
| number of observations | NaN | 2,759 |
| missing values | NaN | 0 |
| % missing values | NaN | 0 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | 135 |
| std | 78.3 |
| min | 0 |
| 25% | 58 |
| 50% | 140 |
| 75% | 204 |
| max | 289 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | -0.0126 |
| std | 0.998 |
| min | -1.2 |
| 25% | -0.625 |
| 50% | -0.341 |
| 75% | 0.458 |
| max | 3.92 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | 0.0367 |
| std | 0.994 |
| min | -2.12 |
| 25% | -0.332 |
| 50% | 0.109 |
| 75% | 1 |
| max | 1 |
| Prediction dataset | |
|---|---|
| distinct values | 5 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 5 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | -0.0232 |
| std | 1.01 |
| min | -6.43 |
| 25% | -0.53 |
| 50% | -0.079 |
| 75% | 0.755 |
| max | 1.35 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 4 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 5 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 3 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | -0.0406 |
| std | 0.939 |
| min | -1.97 |
| 25% | -0.123 |
| 50% | -0.123 |
| 75% | -0.123 |
| max | 8.65 |
| Prediction dataset | |
|---|---|
| distinct values | 4 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 3 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 4 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | 0.0292 |
| std | 1.02 |
| min | -1.79 |
| 25% | -0.672 |
| 50% | -0.157 |
| 75% | 0.625 |
| max | 8.62 |
| Prediction dataset | |
|---|---|
| distinct values | 7 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 4 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | -0.0135 |
| std | 0.976 |
| min | -0.514 |
| 25% | -0.514 |
| 50% | -0.155 |
| 75% | 0.205 |
| max | 17.5 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 8 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 8 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | 0.00369 |
| std | 0.983 |
| min | -1.75 |
| 25% | -0.499 |
| 50% | -0.301 |
| 75% | 0.681 |
| max | 2.74 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | -0.00558 |
| std | 0.975 |
| min | -3.59 |
| 25% | -0.413 |
| 50% | -0.157 |
| 75% | 0.358 |
| max | 1.97 |
| Prediction dataset | |
|---|---|
| distinct values | 6 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | 0.00533 |
| std | 0.919 |
| min | -0.612 |
| 25% | -0.263 |
| 50% | -0.233 |
| 75% | -0.188 |
| max | 14.5 |
| Prediction dataset | |
|---|---|
| distinct values | 9 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 4 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 2 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | 0.0215 |
| std | 1.01 |
| min | -0.896 |
| 25% | -0.815 |
| 50% | -0.654 |
| 75% | 0.992 |
| max | 1.52 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | 0.00312 |
| std | 1.01 |
| min | -2.18 |
| 25% | -0.732 |
| 50% | 0.173 |
| 75% | 0.394 |
| max | 7.58 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | 0.021 |
| std | 1.01 |
| min | -0.903 |
| 25% | -0.823 |
| 50% | -0.628 |
| 75% | 0.965 |
| max | 1.53 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | -0.000406 |
| std | 0.98 |
| min | -2.36 |
| 25% | -0.659 |
| 50% | 0.228 |
| 75% | 0.713 |
| max | 6.05 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | 0.00801 |
| std | 0.994 |
| min | -0.901 |
| 25% | -0.769 |
| 50% | -0.606 |
| 75% | 0.418 |
| max | 2.01 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | -0.0118 |
| std | 0.963 |
| min | -0.439 |
| 25% | -0.351 |
| 50% | -0.285 |
| 75% | -0.0617 |
| max | 10.2 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | 0.0223 |
| std | 1.01 |
| min | -0.88 |
| 25% | -0.806 |
| 50% | -0.675 |
| 75% | 1.03 |
| max | 1.51 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | -0.00618 |
| std | 0.999 |
| min | -2.36 |
| 25% | -0.717 |
| 50% | -0.173 |
| 75% | 0.348 |
| max | 3.79 |
| Prediction dataset | |
|---|---|
| distinct values | 1 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 1 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| distinct values | 8 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | 0.0219 |
| std | 0.989 |
| min | -2.16 |
| 25% | -0.327 |
| 50% | 0.0367 |
| 75% | 0.136 |
| max | 7.12 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | 0.0239 |
| std | 1.01 |
| min | -0.919 |
| 25% | -0.813 |
| 50% | -0.608 |
| 75% | 0.735 |
| max | 1.65 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | -0.0144 |
| std | 0.982 |
| min | -2.76 |
| 25% | -0.875 |
| 50% | 0.21 |
| 75% | 0.839 |
| max | 3.1 |
| Prediction dataset | |
|---|---|
| distinct values | 3 |
| missing values | 0 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | -0.0223 |
| std | 0.993 |
| min | -2.08 |
| 25% | -0.606 |
| 50% | -0.148 |
| 75% | 0.408 |
| max | 9.2 |
| Prediction dataset | |
|---|---|
| count | 2,759 |
| mean | 2,550 |
| std | 1,070 |
| min | 216 |
| 25% | 1,710 |
| 50% | 2,440 |
| 75% | 3,300 |
| max | 7,440 |
Note : the explainability graphs were generated using the test set only.
| True values | Prediction values | |
|---|---|---|
| count | 2,759 | 2,759 |
| mean | 2,550 | 2,560 |
| std | 1,070 | 930 |
| min | 216 | 508 |
| 25% | 1,710 | 1,820 |
| 50% | 2,440 | 2,440 |
| 75% | 3,300 | 3,230 |
| max | 7,440 | 6,880 |
MAE : 355
R2 : 0.783
MSE : 250,000
MAPE : 0.171
MdAE : 251
Explained Variance : 0.783